

logitord(repeated)                           R Documentation

_O_r_d_i_n_a_l _R_a_n_d_o_m _E_f_f_e_c_t_s _M_o_d_e_l_s _w_i_t_h _D_r_o_p_o_u_t_s

_D_e_s_c_r_i_p_t_i_o_n_:

     `logitord' fits an longitudinal ordinal model in dis-
     crete time to outcomes and a logistic model to the
     probability of dropping out using a common random
     effect for the two.

_U_s_a_g_e_:

     logitord(y, id, out.ccov=NULL, drop.ccov=NULL, tvcov=NULL,
             out.tvcov=!is.null(tvcov), drop.tvcov=!is.null(tvcov),
             pout, pdrop, prand.out, prand.drop,
             random.out.int=T, random.out.slope=!is.null(tvcov),
             random.drop.int=T, random.drop.slope=!is.null(tvcov),
             binom.mix=5, fcalls=900, eps=0.0001, print.level=0)

_A_r_g_u_m_e_n_t_s_:

       y: A vector of binary or ordinal responses with lev-
          els 1 to k and 0 indicating drop-out.

      id: Identification number for each individual.

out.ccov: A vector, matrix, or model formula of time-con-
          stant covariates for the outcome regression, with
          variables having the same length as y.

drop.ccov: A vector, matrix, or model formula of time-con-
          stant covariates for the drop-out regression, with
          variables having the same length as y.

   tvcov: One time-varying covariate vector.

out.tvcov: Include the time-varying covariate in the outcome
          regression.

drop.tvcov: Include the time-varying covariate in the drop-
          out regression.

    pout: Initial estimates of the outcome regression coef-
          ficients, with length equal to the number of lev-
          els of the response plus the number of covariates
          minus one.

   pdrop: Initial estimates of the drop-out regression coef-
          ficients, with length equal to one plus the number
          of covariates.

prand.out: Optional initial estimates of the outcome random
          parameters.

prand.drop: Optional initial estimates of the drop-out ran-
          dom parameters.

random.out.int: If TRUE, the outcome intercept is random.

random.out.slope: If TRUE, the slope of the time-varying
          covariate is random for the outcome regression
          (only possible if a time-varying covariate is sup-
          plied and if out.tvcov and random.out.int are
          TRUE).

random.drop.int: If TRUE, the drop-out intercept is random.

random.drop.slope: If TRUE, the slope of the time-varying
          covariate is random for the drop-out regression
          (only possible if a time-varying covariate is sup-
          plied and if drop.tvcov and random.drop.int are
          TRUE).

binom.mix: The total in the binomial distribution used to
          approximate the normal mixing distribution.

  fcalls: Number of function calls allowed.

     eps: Convergence criterion.

print.level: If 1, the iterations are printed out.

_V_a_l_u_e_:

     A list of class `logitord' is returned.

_A_u_t_h_o_r_(_s_)_:

     T.R. Ten Have and J.K. Lindsey

_R_e_f_e_r_e_n_c_e_s_:

     Ten Have, T, Kunselman, A.R., Pulkstenis, E.P. and Lan-
     dis, J.R.  (1998) Biometrics 54, 367-383, for the
     binary case.

_E_x_a_m_p_l_e_s_:

     y <- trunc(runif(20,max=4))
     id <- gl(4,5)
     age <- rpois(20,20)
     times <- rep(1:5,4)
     logitord(y, id=id, out.ccov=~age, drop.ccov=age, pout=c(1,0,0),
             pdrop=c(1,0))
     logitord(y, id, tvcov=times, pout=c(1,0,0), pdrop=c(1,0))

